Evaluating driving control systems for elegant driving

ABSTRACT

In particular embodiments, a computing system may determine a measured driving characteristic of a driving control system based on observations of vehicles driven by the driving control system. The system may determine a difference between the measured driving characteristic and a target driving characteristic, which is based on objectivations of one or more manually controlled vehicles. The system may determine an evaluation objective for the driving control system. The system may determine a weight function for the evaluation objective. The system may determine a score for the driving control system with respect to the evaluation objective by weighting the difference between the measured driving characteristic and the target driving characteristic using the weight function. The system may apply, based on the score, an adjustment to the driving control system to reduce a difference between a subsequently measured driving characteristic of the driving control system and the target driving characteristic.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 16/583,109, filed 25 Sep. 2019.

BACKGROUND

Different levels of driving by vehicles with a driving control systemare becoming increasingly commonplace. Driving control systems includeseveral hardware and software modules to perform different aspects ofdriving a vehicle. Successful driving using a driving control systeminvolves a multitude of potential vehicle actions and while a particulardriving control system may get a person to their destination, thequality of the ride may be neither comfortable nor elegant. The conceptof elegance may be considered the achievement of “human-like” drivingexperience. For example, a ride in a vehicle may be consideredcomfortable, but not elegant. As an example and not by way oflimitation, a vehicle, when taking a turn, it may be comfortable to takethe turn at 10 miles per hour, but it may be elegant to take the sameturn at 25 miles per hour. These considerations may affect whether thedriving control system directs the vehicle to accelerate, maintainspeed, or decelerate while navigating a particular section of a route.The hardware and software modules lead to different types of drivingcharacteristics. Based on changes in these driving characteristics, newversions of the driving control system may be updated and testedperiodically evaluated.

Based in part on these considerations, different versions of drivingcontrol systems may be tested, but there is a lack a statistical methodfor evaluating driving performance, identifying areas of improvement forthe driving control system, and adjusting driving control systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example vehicle navigating a route.

FIG. 2 an example network architecture for evaluating driving controlsystems.

FIG. 3 illustrates an example partitioning of a route into a number ofsegments.

FIGS. 4A-4C illustrate example comparisons of distributions of drivingmetrics.

FIGS. 5A-5C illustrate example computations of weighted distributions ofdriving metrics.

FIG. 6 illustrates example distributions of driving metrics betweendifferent versions of a driving control system.

FIG. 7 illustrates an example scoring of driving metrics.

FIG. 8 illustrates an example method for adjusting a driving controlsystem of a vehicle.

FIGS. 9A-9C illustrate an example of a transportation management vehicledevice.

FIG. 10 illustrates an example block diagram of a transportationmanagement environment.

FIG. 11 illustrates an example block diagram of a transportationmanagement environment for matching ride requestors with autonomousvehicles.

FIG. 12 illustrates an example of a computing system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described. In addition, the embodiments disclosedherein are only examples, and the scope of this disclosure is notlimited to them. Particular embodiments may include all, some, or noneof the components, elements, features, functions, operations, or stepsof the embodiments disclosed above. Embodiments according to theinvention are in particular disclosed in the attached claims directed toa method, a storage medium, a system and a computer program product,wherein any feature mentioned in one claim category, e.g., method, canbe claimed in another claim category, e.g., system, as well. Thedependencies or references back in the attached claims are chosen forformal reasons only. However, any subject matter resulting from adeliberate reference back to any previous claims (in particular multipledependencies) can be claimed as well, so that any combination of claimsand the features thereof are disclosed and can be claimed regardless ofthe dependencies chosen in the attached claims. The subject-matter whichcan be claimed comprises not only the combinations of features as setout in the attached claims but also any other combination of features inthe claims, wherein each feature mentioned in the claims can be combinedwith any other feature or combination of other features in the claims.Furthermore, any of the embodiments and features described or depictedherein can be claimed in a separate claim and/or in any combination withany embodiment or feature described or depicted herein or with any ofthe features of the attached claims.

Subject matter described herein is generally directed to measuring anelegance of a particular version of a driving control system byindicating how similar to human driving the performance of the drivingcontrol system is or may be used during the real-time operation toimprove passenger comfort. In particular embodiments, adjustments to thedriving control system may be made that are based on the identifiedareas of improvement. In addition, the model may be used to determineone or more adjustments to a particular version of the driving controlsystem based on a comparison of the driving characteristics of thevehicles having a driving control system to desired drivingcharacteristics, as described in more detail below. In particularembodiments, the driving characteristics of manually controlled (e.g.,having a human driver) vehicles may be used as a desired drivingcharacteristic. Designing a driving control system that is able to mimicthe driving characteristics of a human driver is particularlychallenging. Herein reference to vehicles using driving control systemsor autonomous control systems may encompass vehicles equipped with fullyautonomous systems, advanced driver-assistance systems (ADAS), or othersemi-autonomous computing systems that include one or more elements ofperception, prediction, and path planning functionality, whereappropriate. An autonomous control system may include software elements(e.g., perception, prediction, autonomy, planning, and control softwaremodules), hardware elements (e.g., computing systems hosting thesoftware modules, sensors, communication network infrastructure,actuator controllers, etc.), and mapping information that is necessaryfor the operation of the driving control system. Different “versions” ofa driving control system may mean that one or more software, hardware,or knowledge aspects of an autonomous or semi-autonomous control systemmay be different than another driving control system. For example, adifferent prediction algorithm or planning algorithm/model may beimplemented in a particular autonomy system while the hardware modulesand other software modules remain the same as a previous version.Similarly, one or more aspects of the hardware system may be updated orchanged while an autonomy stack (perception, prediction, planning, andcontrol modules) making driving decisions for the vehicle remains thesame. Many different versions of the driving control systems may bedeveloped and tested in order to develop the best possible autonomouscontrol system for a vehicle. Furthermore, reference to a softwaremodule may refer to algorithms, such as for example, machine-learning(ML), artificial intelligence (AI), deep learning, heuristics, logic, orany suitable set of instructions, where appropriate.

While these driving metrics are important considerations for successfulnavigation, these factors may be difficult to quantify and use toevaluate the performance of one version of a driving control system toanother. Embodiments below describe using a pre-determined distributionof data to statistically evaluate the performance of a driving controlsystem. Herein reference to driving metrics may encompass values of datathat is measured by the sensors (e.g., acceleration, deceleration, orsteering angle) or derived from the data measured by the sensors (e.g.,distance to objects (or road features) or jerk (acceleration persecond)), where appropriate. Furthermore, reference to a drivingcharacteristic may refer to a characteristic represented by the drivingmetrics, where appropriate. As an example and not by way of limitation,a driving characteristic may include acceleration, deceleration,distance from a center of a driving lane, distance from nearby objects,amount of centripetal force, steering angle, change of acceleration, oraverage speed over a route. As described below, the data may be measuredusing a variety of sensors (e.g., inertial measurement unit (IMU),radar, camera (optical or infrared), or accelerometer).

In particular embodiments, driving metrics collected from the sensors ofa fleet of manually controlled vehicles may be used to understandelegant driving behavior. The driving metrics of vehicles having adriving control system may be benchmarked and compared to drivingmetrics of the manually controlled vehicles to benchmark and comparedriving algorithms to determine the elegance of different drivingalgorithms. The evaluation and benchmarking may be made across a fleetof vehicles as opposed to individual vehicles with unique drivingmetrics specific only to that vehicle. As described in more detailbelow, the driving metrics may be assigned to particular ranges and adistribution of the driving metrics may be generated. In particularembodiments, for each of these driving characteristics, the drivingmetrics representing the driving characteristics of the vehicles havinga driving control system may be compared to the driving metricsrepresenting a desired driving characteristic. A goal may be to minimizethe difference the driving metrics representing the drivingcharacteristics of the vehicles having a driving control system and thedriving metrics representing desired driving characteristics. Thedesired driving characteristics may be one or more of drivingcharacteristics of a human driver and a goal may be for the drivingcontrol system to achieve “human-like” driving metrics for one or moredriving characteristics or elegance. Herein reference to elegance mayencompass the concept of matching driving metrics of a driving controlsystem to the driving metrics of a human driver, where appropriate.

FIG. 1 illustrates an example vehicle navigating a route. As illustratedin the example of FIG. 1, a particular scenario 100 may include a numberof vehicles 102 and 106A-106B navigating a portion of a route thatincludes lanes 161A-161C. Herein reference to a scenario encompasses acombination of dynamic, static, and environmental information that avehicle may come in contact with in the world. For example, a particularscenario may represent a type of road segment (e.g., one way,residential road, highway, etc.) or intersection (e.g., 4 way, 3 way,etc.), types of relevant dynamic agents present on the road segment(e.g., a jaywalker, pedestrians, vehicle within 15 yards, bicyclist,etc.), and types of static traffic elements present (e.g., upcoming stopsign, stoplight, etc.). In addition, scenario 100 may includeenvironmental information, such as for example traffic conditions,weather, day of week, or time of day. Furthermore, reference to a routemay encompass any number of different sections of a road or highwaybetween two geographic locations (also referred to as “road segments”).These road segments may divide a drivable surface in any suitable mannerand may be defined in a number of different ways. For example, roadsegments may be the same length (e.g., ⅓ mile), different lengths (e.g.,based on different types of intersections or traffic elements withinthat road segment), based on performance along the drivable surface(e.g., a continuous drivable surface having similar traffic elements,presence of similar dynamic agents, and/or performance by manual and/orautonomous/semi-autonomous driving control system), etc. Vehicle 102navigating a route may include one or more sensors. Vehicle 102 maynavigate various routes and while navigating the routes collect sensordata, as described in more detail below. Vehicle 102 navigating theroutes may be manually controlled or controlled through a drivingcontrol system (e.g. having some level of assisted navigation).Different paths may be more or less elegant based on the choices of theplanning module and, as described in more detail below, differentdriving characteristics play a role in elegance or comfort of the ridewhen navigating these different paths.

As an example and not by way of limitation, in scenario 100, vehicle 102having a driving control system may navigate a route while in lane 161Bof the highway. Lane 161B may also include vehicle 106A and lane 161Cmay include vehicle 106B. Vehicle 102 may use the systems and methods asdescribed in this disclosure to observe vehicles 106A-106B and navigatethe route based on the observation of vehicles 106A-106B and any otherrelevant objects on the route. While navigating a route, the sensors ofvehicle 102 having the driving control system may collect data of thedriving characteristics of one or more driving metrics. As an exampleand not by way of limitation, optical cameras of vehicle 102 may be usedto determine a distance 104 of vehicle 102 to the lane markers on eitherside of vehicle 102. As another example, the radar of vehicle 102 may beused to determine a distance 108 between vehicle 102 and vehicle 106A,while the LiDAR of vehicle 102 may be used to determine a distance 110to vehicle 106B. The driving metrics of other driving characteristics,such as for example velocity, acceleration, braking force, or thedistance from the center of lane 161B, may be measured and recordedwhile navigating the route. As an example and not by way of limitation,an accelerometer may measure the amount of deceleration occurs inresponse to a rapid decrease in distance 108 between vehicle 102 havingthe driving control system and vehicle 106A.

A driving control system or human driver has to perceive the externalenvironment and make numerous decisions based on the perceivedenvironment. There are many ways for a route to be successfullynavigated by a driving control system, but in a particular embodiment, agoal of the driving control system may be to achieve elegance and matchthe driving characteristics of a manually driven vehicle as closely aspossible, as described in more detail below. As an example and not byway of limitation, based on the observation of vehicle 106A, the drivingcontrol system of vehicle 102 may adjust its velocity to match the valueof distance 108 for a manually controlled vehicle navigating the route.As another example, the driving control system of vehicle 102 may adjustits steerage to the maintain a certain distance 104 between vehicle 102and an edge of lane 161B.

FIG. 2 illustrates an example network architecture for evaluatingdriving control systems. In particular embodiments, a driving-controlsystem scoring 220 may receive a variety of input data associated withone or more driving control systems. The input data may be stored in anysuitable fashion such that it is accessible to driving-control systemscoring 220 as needed. In particular embodiments, the input data may bestored as a number of records that include input data organized basedaround the route being navigated. The input data may also be organizedbased around the particular scenario encountered, information regardinga passenger of the vehicle, or any other suitable variable. Theparticular scenario is a combination of dynamic, static, andenvironmental information that a vehicle may encounter while navigatinga route.

Driving-control system scoring 220 may receive an environmentalrepresentation 212 of the environment around a respective vehicle. Theenvironmental representation 212 may include information about theenvironment around the vehicle from before, during, or after the vehiclenavigates a route. In particular embodiments, environmentalrepresentation 212 may include a standardized format to represent, forexample, the presence of other vehicles, persons, or objects (referredto as the objects in the environment, for simplicity) around thevehicle. Objects may include other vehicles, pedestrians, or roadobstacles, and road features may include intersections, drop-offs,center of lane, or lane markings. Environmental representation 212 mayinclude a predicted path of the detected objects in the environment.Also, in the case where the objects have a special meaning, the state ofthe objects may be noted. For example, a parked car may be noted asbeing different from a vehicle traveling in the same direction as thevehicle. As another example, a status of a traffic light (e.g., green,yellow, red, solid, flashing, off) may be noted and used when predictingthe behavior of the identified objects/agents.

In addition to the environmental representation 212, driving-controlsystem scoring 220 may receive vehicle operation data 210 from one ormore sensors of the vehicle having a driving control system. Vehicleoperation data 210 may include information related to the vehicleoperation measured during or recorded after the vehicle navigates aroute. One or more sensors associated with the vehicle may provide dataabout the operation of the vehicle at any given time. The sensors mayinclude one or more sensors that measure a vehicle's speed, throttleposition, brake position, steering angle, tire pressure, fuel or chargelevel (e.g., level of gasoline, diesel, battery), acceleration, jerk, orany combination thereof. The sensors may include one or more sensorsthat output information about the external environment of the vehicle.This may include one or more sensors for measuring temperature,humidity, visibility, road condition, traction, distance to objects(e.g., other vehicles, pedestrians, edges of the road, or otherobjects). Driving metrics are the values of the driving characteristicsmeasured by these sensors.

In particular embodiments, driving-control system scoring 220 maydetermine a distribution of the driving metrics representing aparticular driving characteristic. As an example and not by way oflimitation, driving-control system scoring 220 may determine a range fora driving characteristic corresponding to a distance of the vehicle toother vehicles, the driving metrics may range between 8 to 24 inchesbased on information provided by environmental representation 212. Basedon this range, vehicle-control system scoring 220 may determine a numberof intervals to represent the distribution of the value of the drivingmetrics and assign each value of the driving metric to a correspondinginterval.

In particular embodiments, driving-control system scoring 220 mayreceive one or more weighting functions 214 to be applied to the drivingmetrics representing one or more driving characteristics. Weightingfunctions 214 serve as a filter that may amplify some portions of thedriving characteristics and minimizes other portions of the drivingcharacteristics based on the desirable or undesirable operationalaction, ranges, and information being evaluated. As an example and notby way of limitation, lower values of deceleration may be weighed moreheavily than higher values of deceleration. As another example and notby way of limitation, lower values of deceleration may be weighed moreheavily than higher values of deceleration. In particular embodiments,weighing functions 214 may be based on user evaluation of one or moredriving metrics of a particular driving characteristic. As an exampleand by way of limitation, passengers of a vehicle may be afforded theopportunity to provide feedback about one or more driver metricsexperienced while riding in a vehicle. The feedback may be directed toan entire route or to discrete segments of the route as described inmore detail below. The evaluations from the users may be provided todriving-control system scoring 220 in the form of weighting functions214. As an example and not by way of limitation, for the drivingcharacteristic corresponding to deceleration on a particular route, lowlevels of deceleration may be weighed more heavily than high levels ofbraking force, as high levels of braking force may lead to suboptimalrapid deceleration that may be undesirable and uncomfortable for users.As another example, low levels of deceleration may be weighted moreheavily than high levels of braking force, as high levels of brakingforce may lead to suboptimal rapid deceleration that may be undesirablefor most users. In particular embodiments, one or more passengers may beprovided the opportunity to evaluate different driving characteristicswhile riding in the vehicle.

Driving-control system scoring 220 may compare the drivingcharacteristics of the vehicles having a driving control system todesired driving characteristics. As described in more detail below,driving-control system scoring 220 compares driving metrics of one ormore particular driving characteristics to measured operational metricsassociated with the driving control system. As an example and not bylimitation, for a particular route or segment, the driving metricscorresponding to the distance to other vehicles by vehicles having adriving control system may be compared to the driving metrics of thedesired distance to other vehicles on that route. As another example,the driving metrics corresponding to the velocity by vehicles having adriving control system may be compared to the driving metrics of thedesired velocity on that route. In particular embodiments, thecomparison may be based on comparing different values of the drivingcharacteristics weighed using weighing functions 214.

In particular embodiments, driving-control system scoring 220 mayreceive one or more coefficients 216 to be applied to one or moredriving characteristics to determine a score 230 for a particulardriving control system for a particular route. The values ofcoefficients 216 may weigh particular driving characteristics todetermine score 230. As an example and not by way of limitation, score230 may be represented as a linear equation and calculated as a sum ofthe weighted product for each driving characteristic and its respectivecoefficient 216. Calculating score 230 for the route based on a sum ofthe weighted products. Score 230 may be used to statistically evaluatethe performance of different versions (e.g., hardware modules orsoftware modules) of the driving control system. In particularembodiments, score 230 for a route may be used to determine particularsegments or scenarios (e.g., traffic or time of days) where a particularversion of the driving control system may be underperforming by notproviding an elegant driving experience while navigating the segment orin relation to those scenarios. An overall elegance score for a drivingalgorithm and/or system may be calculated based on the scores 230 formultiple routes or a complete route, a portion of a route, or anysegment of driving activity.

Although this disclosure describes specific types of input data that maybe used by driving-control system scoring 220, these are meant asexamples only and are not limiting. Driving-algorithm scoring system 220may consider any combination or variation of the data types described aswell as any other suitable data that may be used by driving-controlsystem scoring 220 for evaluating different versions of a drivingcontrol system. The input data, and possibly more as appropriate, may beused by driving-control system scoring 220 to generate one or morescores 230. In particular embodiments, driving-control system scoring220 may be a subsystem operating directly on a vehicle. In particularembodiments, driving-control system scoring 220 may be a subsystem of avehicle management system that is involved with the communication and/orcontrol of the vehicle. In particular embodiments, driving-controlsystem scoring 220 may include subsystems of one or more vehicles andthe vehicle management system working in concert to generate theappropriate scores 230 for the vehicle. In some embodiments, score 230may be generated for each particular vehicle having a driving controlsystem. In particular embodiments, score 230 may be generated based on avariety of similarities between one or more vehicles.

FIG. 3 illustrates an example partitioning of a route into a number ofsegments. In particular embodiments, a route 305 may be partitioned intoa number of segments 310A-310C. As an example and not by way oflimitation, route 305 may be partitioned into segments 310A-310C basedon different road features along route 305 (e.g., each stop sign,stoplight, or intersection), pre-determined length (e.g., ¼ mile, 1mile, etc.), driving time (e.g., 5-minute segments), or dividing route305 into a predetermined number of equal-length portions (e.g., every 5segments). As illustrated in the example of FIG. 3, each segment310A-310C may have a different distribution 315A-315C of driving metricsdepending on the scenario at the time the vehicle is navigating aparticular segment 310A-310C (e.g., number of other vehicles, time ofday, weather, or obstacles) or the features of the particular segment310A-310C (e.g., number of turns, number of intersections, or number oftraffic signals). As an example and not by way of limitation, vehiclesnavigating segment 310A may have a distribution 315A of the averagedistance from the center of the lane, while vehicles navigating segment310B may have a distribution 315B of the average distance from thecenter of the lane. Vehicles navigating segment 310C may have adistribution 315C of the average distance from the center of the lane.As described in more detail below, distributions 315A-315C of drivingmetrics for each segment may be used to calculate a score for eachsegment or a score for the overall route based on a combination (e.g.,summation or averaging) of the segment distributions 315A-315C.

As described in more detail above, the sensors of vehicles (having anautonomous or semi-autonomous control system, or manually controlled)collect the driving metrics of one or more driving characteristics whilethe vehicle is navigating route 305. In most cases, there may bedifferences between the driving metrics from manually controlledvehicles and vehicles having a driving control system navigating route305. In addition, different versions of driving control systems (e.g.software or hardware modules), types of vehicles, sensor systems, etc.may have a different distribution of driving metrics and the differentdriving metrics may be tracked over time. In particular embodiments, ascore calculated for each segment 310A-310C. As an example and not byway of limitation, the score for each segment 310A-310C may be summed toderive an overall score for route 305. As described below in moredetail, a distribution of driving metrics may be determined for eachsegment 310A-310C and combined (e.g., summed or weighted) to determine ascore for route 305.

FIGS. 4A-4C illustrate example comparisons of distributions of drivingmetrics. As described in more detail above, driving metrics collectedfrom the sensors of a fleet of vehicles (e.g., manually controlled,autonomous control system, semi-autonomous control system, or anycombination thereof) may be used to understand elegant driving behavior.The driving metrics of vehicles having an autonomous or semi-autonomousdriving control system may be compared to driving metrics of themanually controlled vehicles to benchmark and compare versions of thedriving control system to determine the elegance of the differentversions of the driving control system. In particular embodiments, thedistribution of driving metrics of the desired driving characteristicsmay be based on sensor data measured by manually controlled vehiclesnavigating the route. As an example and not by way of limitation, thedriving metrics for acceleration on a particular route may be determinedfrom the acceleration data measured by the sensors of manuallycontrolled vehicles navigating the particular route.

The driving metrics of manually controlled vehicles may be compared tothe driving metrics of vehicles having a driving control system tobenchmark and compare the elegance of driving control systems tohuman-driving performance. The evaluation of a driving control systemused for a route may be based on a comparison of the driving metrics ofthe vehicles having a driving control system to a pre-determineddistribution of driving metrics. As an example and not by way oflimitation, the pre-determined distribution of the driving metrics maycorrespond to the driving metrics of desired driving characteristics. Inparticular embodiments, the driving metrics of each drivingcharacteristic may be assigned to particular ranges and a histogram ofthe driving metrics 405A-405C may be generated for direct comparison ofthe driving metrics for different driving characteristics.

As illustrated in the example of FIG. 4A, histogram 405A may correspondto the average velocity of vehicles having a driving control systemnavigating a particular route, histogram 415A may correspond to theaverage velocity of manually controlled vehicles navigating a particularroute. In particular embodiments, histogram 415A of driving metrics ofthe desired driving characteristic may be overlaid over histogram 405Aof the driving metrics of vehicles having a driving control system and,for each range, a histogram 410A of the differences between the drivingmetrics of the vehicles having a driving control system and the drivingmetrics of the manually controlled vehicles may be computed for eachdriving characteristic (e.g., average velocity).

As illustrated in the example of FIG. 4B, histogram 405B may correspondto the average distance of vehicles having a driving control system froma center of the lane, histogram 415B may correspond to the averagedistance of manually controlled vehicles from a center of the lane, andhistogram 410B may correspond to the differences in the average distanceof vehicles having a driving control system and manually controlledvehicles from the center of the lane. In particular embodiments,histogram 415B of driving metrics of the desired driving characteristicmay be overlaid over histogram 405B of the driving metrics of vehicleshaving a driving control system and, for each range, a histogram 410B ofthe differences between the average distance to the center of the lanefor the vehicles having a driving control system and the averagedistance to the center of the lane of the manually controlled vehiclesmay be computed.

As illustrated in the example of FIG. 4C, histogram 405C may correspondto the distance of vehicles having a driving control system to thevehicle in front of it, histogram 415C may correspond to the distance ofmanually controlled vehicles to the vehicle in front of it, andhistogram 410C may correspond to the differences in the distance ofvehicles having a driving control system and manually controlledvehicles to the vehicle in front of it. In particular embodiments,histogram 415C of driving metrics of the manually controlled vehiclesmay be overlaid over histogram 405B of the driving metrics of vehicleshaving a driving control system and, for each range, a histogram 410C ofthe differences between the average distance to the vehicle in front ofthe vehicles having a driving control system and the average distance tothe vehicle in front of the manually controlled vehicles may becomputed.

FIGS. 5A-5C illustrate example computations of weighted distributions ofdriving metrics. Analyzing the differences of the driving metrics410A-410C may identify problematic issues with the algorithms of theplanning module of a driving control system and identify particularmaneuvers, road segments, and/or scenarios that are difficult for thedriving control system to handle. Weighting functions 114A-114C may beasymmetric for particular driving characteristics. In particularembodiments, weighted differences 520A-520C may be used to determine thescore of the segment or route. Weighting functions 114A-114C may betailored to generate the score differently depending on the purpose orgoal of the evaluation (e.g., elegance vs. a more comfortable ride). Asan example and not by way of limitation, higher values of distancebetween a vehicle and a drop-off may be weighed more heavily (or beconsidered more desirable) than lower values of distance between thevehicle and an edge of a road (e.g., a sidewalk or a drop-off on theside of the road). In particular embodiments, passenger evaluations maybe used as a basis for one or more weighting functions 114A-114C Inparticular embodiments, a goal may be to minimize differences 410A-410Cbetween the driving metrics representing the driving characteristics ofthe vehicles having a driving control system and the driving metricsrepresenting desired driving characteristics. Although FIGS. 5A-5Cdescribe and illustrate particular driving characteristics, thisdisclosure contemplates any suitable driving characteristics related toperformance of a vehicle or relative to a map feature, such as forexample, an amount of deceleration, acceleration, rate of acceleration,steering angle, angular momentum, velocity, distance to nearest objectin path, distance to lane edge or boundary, distance from center oflane, distance into an intersection, or a perception the driving of thevehicle by other drivers (e.g., based on a frequency of car horn use byother vehicles).

As illustrated in the example of FIG. 5A, histogram 410A correspondingto the difference between the average velocity of the vehicles having adriving control system and the average velocity of the manuallycontrolled vehicles may be weighed using weighting function 114A,thereby resulting in a histogram 420A of the weighted difference betweenthe average velocity between of the vehicles having a driving controlsystem and the manually controlled vehicles. In particular embodiments,weighting functions 114A-114C (e.g., step function) may be applied orconvolved with histograms 410A-410C to determine a weighted differences520A-520C of the segment or route.

As illustrated in the example of FIG. 5A, histogram 410A correspondingto the difference between the average velocity of the vehicles having adriving control system and the average velocity of the manuallycontrolled vehicles may be weighed using weighting function 114A,thereby resulting in a histogram 520A of the weighted difference betweenthe average velocity between of the vehicles having a driving controlsystem and the manually controlled vehicles. In particular embodiments,weighting functions 114A-114C (e.g., step function) may be applied orconvolved with histograms 410A-410C to determine a weighted differences520A-520C of the segment or route. Similarly, as illustrated in theexample of FIG. 5B, histogram 410B corresponding to the differencebetween the average distance to the center of the lane of vehicleshaving a driving control system and the average distance to the centerof the lane of the manually controlled vehicles may be weighed usingweighting function 114B, thereby resulting in a histogram 520B of theweighted difference between the average distance to the center of thelane of the vehicles having a driving control system and the manuallycontrolled vehicles. As another example illustrated in FIG. 5C,histogram 410C corresponding to the difference between the averagedistance to the vehicles in front of vehicles having a driving controlsystem and the average distance to the vehicles in front of the manuallycontrolled vehicles may be weighed using weighting function 114C,thereby resulting in a histogram 520C of the weighted difference betweenthe average distance to the vehicles in front of the vehicles having adriving control system and vehicles in front of the manually controlledvehicles.

FIG. 6 illustrate example distributions of driving metrics betweendifferent versions of a driving control system. In particularembodiments, different versions of a driving control system may becompared based on the distributions of driving metrics of a vehiclenavigating a route 305. As illustrated in the example of FIG. 6, aparticular version (e.g., “version A”) of the driving control system mayhave particular distributions 315A-315C of driving metrics for eachsegment 310A-310C of route 305. Furthermore, another version (e.g.,“version B”) of the control system may have different distributions615A-615C of driving metrics for each segment 310A-310C of route 305. Inparticular embodiments, histograms 315A-315C of driving metrics of the“version A” of the driving control system may be overlaid over thecorresponding histograms 615A-615C of the “version B” of the drivingcontrol system, so that the driving metrics of the respective versionsof the driving control system may be compared.

FIG. 7 illustrates an example scoring of driving metrics for a route. Inparticular embodiments, a score for evaluating a particular drivingalgorithm for a number of segments may be calculated. As described inmore detail above, a set of weighted differences 720A-720C of thedriving metrics representing the driving characteristics of the vehicleshaving a driving control system and the driving metrics representingdesired driving characteristics may be determined for a number ofsegments. As illustrated in the example of FIG. 7, a score 710A-710C foreach segment may be calculated using coefficients applied to one or moredriving characteristics and weighted differences 720A-720C. The valuesof the coefficients may weigh particular driving characteristics (e.g.,average velocity) more than others (e.g., distance to the center of alane). As an example and not by way of limitation, each segment score710A-710C may be represented as a linear equation and calculated as asum of the weighted products for each driving characteristic and itsrespective coefficient. As illustrated in the example of FIG. 7, a score730 for a route may be calculated using scores 710A-710C of theindividual segments that form the route. As an example and not by way oflimitation, score 730 of the route may be a sum of scores 710A-710C ofthe segments, an average of scores 710A-710C of the segments, weightedsum, weighted average, or any suitable operation.

In particular embodiments, score 730 of a route may be used to evaluatea particular version of a driving control system for a route may becalculated. Score 730 of a route or score 720A-720C of individualsegments may be calculated in real-time as soon as the route or segmentis completed. In particular embodiments, during operation, score 730 maybe calculated after a segment is completed to evaluate the performancefor that segment in relation to known and aggregated behavior on thatsegment. As an example and not by way of limitation, the score for anumber of segments may be above a pre-determined threshold valueindicating the driving algorithm has achieved reasonable matching to ahuman driver navigating the same route, but a particular segment of theroute may have score 730 that is below the pre-determined thresholdvalue. The score below the threshold value may indicate especially poormatching of the driving metrics for one or more driving characteristicsby the driving control system for the particular segment. In this case,this poor matching may indicate especially suboptimal performance by thedriving control system and the vehicle may be flagged for servicing orpulled off the road.

In particular embodiments, different versions of the driving controlsystem (e.g., an updated or different version of a prediction softwaremodule, the implementation of a new model of a sensor with the existingautonomous software stack, any combination of software and/or hardwareupdates, etc.) may be used for each segment of a route and the relativeperformance of each driving algorithm may be evaluated and comparedbased on their relative scores. As an example and not by way oflimitation, a number of versions of a driving control system may beranked based on their score for one or more segments navigating one ormore segments. In particular embodiments, score 730 or ranking may beused to identify particular versions of the driving control system thatresults in the best operation of the vehicle for a particular route orsegment. As an example and not by way of limitation, a direct comparisonbased on score 730 may be made between two particular versions ofparticular LiDAR sensor tested on a route. As another example, differentversions of a prediction software module may be compared based on aranking for a particular segment. In particular embodiments, differentversions of different modules that are better at performing on aparticular route or segment may be used or updated based on theperformance evaluation. As an example and not by way of limitation, aparticular model of a LiDAR sensor may be used for vehicles having adriving control system that operates on a particular route. When aplanning module selects a route that includes these segments, thevehicle may use the highest-ranked version of the planning software forthose particular segments. In this way, the elegance of the travel alongthese segments may be optimized.

Especially poor performance by a particular vehicle or version of adriving control system may be identified based on score 730 relative toscore 730 for other vehicles or versions of a driving control system.The score may be used to identify issues with a planning module used bythe driving control systems and identify particular maneuvers, roadsegments, and/or scenarios that are problematic for particular versionsof the driving control system. As an example and not by way oflimitation, one or more segments of underperformance by the vehicleshaving a driving control system may be identified based on segment score710A-710B of the identified route segments being below a pre-determinedthreshold value. In particular embodiments, adjustments may be made tothe driving algorithm for segments with relatively low scores or scoresbelow the pre-determined threshold value. As such, the results may helpidentify particular road conditions, obstacles, or scenarios that aredifficult for the driving control system and allow for targeteddevelopment and training to fix the associated errors and problems withthat particular road segment. In particular embodiments, adjustments toa particular version of a driving control system (e.g., softwaremodules, hardware modules, or a combination of both) may be made tominimize weighted differences 710A-710C between the histograms of thevehicles having a driving control system and the histograms of themanually controlled vehicles. The adjusted version of the drivingcontrol system may be provided to one or more vehicles having a drivingcontrol system.

FIG. 8 illustrates an example method for adjusting a driving controlsystem of a vehicle. The method 800 may begin at step 810, a computingsystem may access driving metrics of a vehicle using a driving controlsystem to navigate a route. At step, 820, the computing system maycompute, based on the driving metrics, a driving characteristic for thevehicle having a driving control system. In particular embodiments, thedriving characteristic is represented by the driving metrics. Inparticular embodiments, at step 825, the computing system may apply aweighting function to the respective driving metrics. As an example andnot by way of limitation, the weighting function may be based onevaluations of passengers of vehicles driving the route. At step 830,the computing system may compare the driving characteristic of thevehicle having a driving control system to a driving characteristic of amanually controlled vehicle represented by the number of drivingmetrics. In particular embodiments, the driving metrics of the manuallycontrolled vehicles are collected from a large number of manuallycontrolled vehicles and used in the comparison to minimize noise oroutlier data for any particular scenario.

In particular embodiments, at step 835, the computing system mayidentify if there exist one or more segments of the route where aparticular version of the driving control system has underperformed. Inparticular embodiments, segments of underperformance may be identifiedbased on the score of the routes having a score below a threshold value.In particular embodiments, the computing system may rank the currentversion of the driving control system with previous versions of thedriving control system. In addition, the score may be used to identifyparticularly difficult segments or scenarios or problematic aspects ofproviding an elegant experience for a planning module. In particularembodiments, versions of the driving control system may be evaluatedbased on different goals (e.g., elegance, efficiency, or comfort) thatare calculated using different scoring algorithms and measured overdifferent conditions (e.g., over routes, particular scenarios,particular types of obstacles or maneuvers, etc.). At step 840, thecomputing system may modify the driving control system based on thecomparison, described in step 830. In particular embodiments, themodifications may be based on the difference between the driving metricsof the vehicle having a driving control system and the driving metricsof the desired driving characteristics. At step 850, the computingsystem may provide the modifications to the driving control system tothe vehicle.

Particular embodiments may repeat one or more steps of the method ofFIG. 8, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 8 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 8 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method foradjusting a driving control system of a vehicle including the particularsteps of the method of FIG. 8, this disclosure contemplates any suitablemethod for adjusting a driving control system of a vehicle including anysuitable steps, which may include all, some, or none of the steps of themethod of FIG. 8, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 8, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 8.

FIGS. 9A-9C show an example transportation management vehicle device 960in accordance with embodiments described herein. The transportationmanagement vehicle device 960 may include a front view 902 (FIG. 9A) anda rear view 908 (FIG. 9B). In particular embodiments, the front view 902may be designed to face the outside of the vehicle so that it is visibleto, e.g., ride requestors, and the rear view 908 may be designed to facethe interior of the vehicle so that it is visible to, e.g., thepassengers. As shown in FIG. 9A, a front view 902 of the transportationmanagement vehicle device 960 may include a front display 904. Inparticular embodiments, the front display 904 may include a secondaryregion or separate display 906. As shown in FIG. 9A, the front display904 may include various display technologies including, but not limitedto, one or more liquid crystal displays (LCDs), one or more arrays oflight emitting diodes (LEDs), AMOLED, or other display technologies. Inparticular embodiments, the front display 904 may include a cover thatdivides the display into multiple regions. In particular embodiments,separate displays may be associated with each region. In particularembodiments, the front display 904 may be configured to show colors,text, animation, patterns, color patterns, or any other suitableidentifying information to requestors and other users external to aprovider vehicle (e.g., at a popular pick-up location, requestors mayquickly identify their respective rides and disregard the rest based onthe identifying information shown). In particular embodiments, thesecondary region or separate display 906 may be configured to displaythe same, or contrasting, information as front display 904.

FIG. 9B shows an embodiment of the rear view 908 of the transportationmanagement vehicle device 960. As shown, the rear view 908 in particularembodiments may include a rear display 910. As with the front display904, the rear display 910 may include various display technologiesincluding, but not limited to, one or more liquid crystal displays(LCDs), one or more arrays of light emitting diodes (LEDs), AMOLED, orother display technologies. The rear display 980 may be configured todisplay information to the provider, the requestor, or other passengersin the passenger compartment of the vehicle. In particular embodiments,rear display 910 may be configured to provide information to people whoare external to and behind the provider vehicle. Information may beconveyed via, e.g., scrolling text, color, patterns, animation, and anyother visual display. As further shown in FIG. 9B, the transportationmanagement vehicle device 960 may include a power button 912 or anyother suitable user interface that can be used to turn the device 960 onor off In particular embodiments, power button 912 may be a hardwarebutton or switch that physically controls whether power is provided tothe transportation management vehicle device 960. Alternatively, powerbutton 912 may be a soft button that initiates a startup/shutdownprocedure managed by software and/or firmware instructions. Inparticular embodiments, the transportation management vehicle device 960may not include a power button 912. Additionally, the transportationmanagement vehicle device 960 may include one or more light features 914(such as one or more LEDs or other light sources) configured toilluminate areas adjacent to the device 960 and/or provide statussignals.

In particular embodiments, the transportation management vehicle device960 may include a connector 916. In particular embodiments, theconnector 916 may be configured to physically connect to the rideprovider's computing device and/or the requestor's computing device. Inparticular embodiments, the connector 916 may be configured forphysically connecting the transportation management vehicle device 960to the vehicle for power and/or for communicating with the vehicle. Inone instance, the connector 916 may implement the CAN (Controller AreaNetwork) bus interface or any other suitable communication interface orprotocol for communicating with a vehicle. In another instance, theconnector 916 may include a CAN bus interface that may be utilized incommunicating with a vehicle. For example, the CAN bus interface mayinterface with an on-board diagnostics (OBD) port (e.g., an OBD-I port,an OBD-II port, etc.) of the vehicle. In particular embodiments, throughthe connector 916, the transportation management vehicle device 960 maybe able to issue instructions to the vehicle's onboard computer andcause it to adjust certain vehicle configurations, such asair-conditioning level, entertainment/informational content (e.g.,music, news station, content source, etc.), audio volume, windowconfiguration, seat warmer temperature, and any other configurablefeatures of the vehicle. As another example, the connector 916 mayenable the transportation management vehicle device 960 to query thevehicle for certain data, such as current configurations of any of theaforementioned features, as well as the vehicle's speed, fuel level,tire pressure, external temperature gauge, navigation system, and anyother information available through the vehicle's computing system. Inparticular embodiments, the transportation management vehicle device 960may be further configured with wireless communication capabilities(e.g., Bluetooth, WI-FI, NFC, etc.), thereby enabling the device 960 towirelessly communicate with the vehicle, the provider's computingdevice, and/or the requestor's computing device.

In particular embodiments, the transportation management vehicle device960 may be integrated with one or more sensors 919, such as a camera,microphone, infrared sensor, gyroscope, accelerometer, and any othersuitable sensor for detecting signals of interest within the passengercompartment of the vehicle. For example, the sensor 919 may be arear-facing wide-angle camera that captures the passenger compartmentand any passengers therein. As another example, the sensor 919 may be amicrophone that captures conversation and/or sounds in the passengercompartment. The sensor 919 may also be an infrared sensor capable ofdetecting motion and/or temperature of the passengers.

Although FIG. 9B illustrates particular numbers of components (e.g., asingle sensor 919, a single display 910, a single connector 916, etc.),one of ordinary skill in the art would appreciate that any suitablenumber of each type of component may be included in the transportationmanagement vehicle device 960. For example, in particular embodiments, atransportation management vehicle device 960 may include one or more ofa camera, microphone, and infrared sensor. As another example, thedevice 960 may include one or more communication interfaces, whetherwired or wireless.

FIG. 9C shows a block diagram of various components of a transportationmanagement vehicle device 960 in accordance with particular embodiments.As shown in FIG. 9C, the transportation management vehicle device 960may include a processor 918. Processor 918 may control informationdisplayed on rear display 910 and front display 904. As describedherein, each display may be designed to display information to differentintended users, depending on the positioning of the users and thetransportation management vehicle device 960. In particular embodiments,display data 920 may include stored display patterns, sequences, colors,text, animation or other data to be displayed on the front and/or reardisplay. The display data 920 may also include algorithms for generatingcontent and controlling how it is displayed. The generated content, forexample, may be personalized based on information received from thetransportation management system, any third-party system, the vehicle,and the computing devices of the provider and/or requestor. Inparticular embodiments, display data 920 may be stored on a hard diskdrive, solid-state drive, memory, or any other storage device.

In particular embodiments, lighting controller 922 may manage the colorsand/or other lighting displayed by light features 914, the front display904, and/or the back display 910. The lighting controller may includerules and algorithms for controlling the lighting features 914 so thatthe intended information is conveyed. For example, to help a set ofmatching provider and requestor find each other at a pick-up location,the lighting controller 922 may obtain instructions that the color blueis to be used for identification. In response, the front display 904 maydisplay blue and the lighting controller 922 may cause the lightfeatures 914 to display blue so that the ride provider would know whatcolor to look for.

In particular embodiments, the transportation management vehicle device960 may include a communication component 924 for managingcommunications with other systems, including, e.g., the provider device,the requestor device, the vehicle, the transportation management system,and third-party systems (e.g., music, entertainment, traffic, and/ormaps providers). In particular embodiments, communication component 924may be configured to communicate over WI-FI, Bluetooth, NFC, RF, or anyother wired or wireless communication network or protocol.

In particular embodiments, ride-service device 960 may include aninput/output system 926 configured to receive inputs from users and/orthe environment and provide output. For example, I/O system 926 mayinclude a sensor such as an image-capturing device configured torecognize motion or gesture-based inputs from passengers, a microphoneconfigured to detect and record speech or dialog uttered, a heat sensorto detect the temperature in the passenger compartment, and any othersuitable sensor. The I/O system 926 may output the detected sensor datato any other system, including the transportation management system, thecomputing devices of the ride provider and requestor, etc. Additionally,I/O system 926 may include an audio device configured to provide audiooutputs (such as alerts, instructions, or other information) to usersand/or receive audio inputs, such as audio commands, which may beinterpreted by a voice recognition system or any other commandinterface. In particular embodiments, I/O system 926 may include one ormore input or output ports, such as USB (universal serial bus) ports,lightning connector ports, or other ports enabling users to directlyconnect their devices to the transportation management vehicle device960 (e.g., to exchange data, verify identity information, provide power,etc.).

FIG. 10 shows a transportation management environment 1000, inaccordance with particular embodiments. For example, a transportationmanagement system 1002 executing on one or more servers or distributedsystems may be configured to provide various services to ride requestorsand providers. In particular embodiments, the transportation managementsystem 1002 may include software modules or applications, including,e.g., identity management services 1004, location services 1006, rideservices 1008, and/or any other suitable services. Although a particularnumber of services are shown as being provided by system 1002, more orfewer services may be provided in various embodiments. In addition,although these services are shown as being provided by the system 1002,all or a portion of any of the services may be processed in adistributed fashion. For example, computations associated with a servicetask may be performed by a combination of the transportation managementsystem 1002 (including any number of servers, databases, etc.), one ormore devices associated with the provider (e.g., devices integrated withthe managed vehicles 1014, provider's computing devices 1016 and tablets1020, and transportation management vehicle devices 1018), and/or one ormore devices associated with the ride requestor (e.g., the requestor'scomputing devices 1024 and tablets 1022). In particular embodiments, thetransportation management system 1002 may include one or more generalpurpose computers, server computers, distributed computing systems,clustered computing systems, cloud-based computing systems, or any othercomputing systems or arrangements of computing systems. Thetransportation management system 1002 may be configured to run any orall of the services and/or software applications described herein. Inparticular embodiments, the transportation management system 1002 mayinclude an appropriate operating system as well as various serverapplications, such as web servers capable of handling hypertexttransport protocol (HTTP) requests, file transfer protocol (FTP)servers, database servers, etc.

In particular embodiments, identity management services 1004 may beconfigured to, e.g., perform authorization services for requestors andproviders and manage their interactions and data with the transportationmanagement system 1002. This may include, e.g., authenticating theidentity of providers and determining that they are authorized toprovide services through the transportation management system 1002.Similarly, requestors' identities may be authenticated to determinewhether they are authorized to receive the requested services throughthe transportation management system 1002. Identity management services1004 may also manage and control access to provider and requestor datamaintained by the transportation management system 1002, such as drivingand/or ride histories, vehicle data, personal data, preferences, usagepatterns as a ride provider and as a ride requestor, profile pictures,linked third-party accounts (e.g., credentials for music orentertainment services, social-networking systems, calendar systems,task-management systems, etc.) and any other associated information. Themanagement service 1004 may also manage and control access toprovider/requestor data stored with and/or obtained from third-partysystems. For example, a requester or provider may grant thetransportation management system 1002 access to a third-party email,calendar, or task management system (e.g., via the user's credentials).As another example, a requestor or provider may grant, through a mobiledevice (e.g., 1016, 1020, 1022, and 1024), a transportation applicationassociated with the transportation management system 1002 access to dataprovided by other applications installed on the mobile device. Such datamay be processed on the client and/or uploaded to the transportationmanagement system 1002 for processing, if so desired.

In particular embodiments, the transportation management system 1002 mayprovide location services 1006, which may include navigation and/ortraffic management services and user interfaces. For example, thelocation services 1006 may be responsible for querying devicesassociated with the provider (e.g., vehicle 1014, computing device 1016,tablet 1020, transportation management vehicle device 1018) and therequester (e.g., computing device 1024 and tablet 1022) for theirlocations. The location services 1006 may also be configured to trackthose devices to determine their relative proximities, generate relevantalerts (e.g., proximity is within a threshold distance), generatenavigation recommendations, and any other location-based services.

In particular embodiments, the transportation management system 1002 mayprovide ride services 1008, which may include ride matching andmanagement services to connect a requestor to a provider. For example,after the identity of a ride requestor has been authenticated by theidentity management services module 1004, the ride services module 1008may attempt to match the requestor with one or more ride providers. Inparticular embodiments, the ride services module 1008 may identify anappropriate provider using location data obtained from the locationservices module 1006. The ride services module 1008 may use the locationdata to identify providers who are geographically close to the requestor(e.g., within a certain threshold distance or travel time) and furtheridentify those who are a good match with the requestor. The rideservices module 1008 may implement matching algorithms that scoreproviders based on, e.g.: preferences of providers and requestors;vehicle features, amenities, condition, and status; provider's preferredgeneral travel direction, range of travel, and availability; requestor'sorigination and destination locations, time constraints, and vehiclefeature needs; and any other pertinent information for matchingrequestors with providers. In particular embodiments, the ride services1008 may use rule-based algorithms or machine-learning models formatching requestors and providers.

The transportation management system 1002 may communicatively connect tovarious devices through networks 1010 and 1012. Networks 1010, 1012 mayinclude any combination of interconnected networks configured to sendand/or receive data communications using various communication protocolsand transmission technologies. In particular embodiments, networks 1010,1012 may include local area networks (LAN), wide-area network, and/orthe Internet, and may support communication protocols such astransmission control protocol/Internet protocol (TCP/IP), Internetpacket exchange (IPX), systems network architecture (SNA), and any othersuitable network protocols. In particular embodiments, data may betransmitted through networks 1010, 1012 using a mobile network (such asa mobile telephone network, cellular network, satellite network, oranother mobile network), PSTNs (a public switched telephone networks),wired communication protocols (e.g., USB, CAN), and/or wirelesscommunication protocols (e.g., WLAN technologies implementing the IEEE802.11 family of standards, Bluetooth, Bluetooth Low Energy, NFC,Z-Wave, and ZigBee). In particular embodiments, networks 1010, 1012 mayeach include any combination of networks described herein or known toone of ordinary skill in the art.

In particular embodiments, devices within a vehicle may beinterconnected. For example, any combination of the following may becommunicatively connected: vehicle 1014, provider computing device 1016,provider tablet 1020, transportation management vehicle device 1018,requestor computing device 1024, requestor tablet 1022, and any otherdevice (e.g., smart watch, smart tags, etc.). For example, thetransportation management vehicle device 1018 may be communicativelyconnected to the provider computing device 1016 and/or the requestorcomputing device 1024. The transportation management vehicle device 1018may connect 1026, 1028 to those devices via any suitable communicationtechnology, including, e.g., WLAN technologies implementing the IEEE802.11 family of standards, Bluetooth, Bluetooth Low Energy, NFC,Z-Wave, ZigBee, and any other suitable short-range wirelesscommunication technology.

In particular embodiments, users may utilize and interface with one ormore services provided by the transportation management system 1002using applications executing on their respective computing devices(e.g., 1014, 1016, 1018, and/or 1020), which may include mobile devices(e.g., an iPhone®, an iPad®, mobile telephone, tablet computer, apersonal digital assistant (PDA)), laptops, wearable devices (e.g.,smart watch, smart glasses, head mounted displays, etc.), thin clientdevices, gaming consoles, and any other computing devices. In particularembodiments, provider computing device 1014 may be an add-on device tothe vehicle, such as a vehicle navigation system, or a computing devicethat is integrated with the vehicle, such as the management system of anautonomous vehicle. The computing device may run on any suitableoperating systems, such as Android®, iOS®, macOS®, Windows®, Linux®,UNIX®, or UNIX®-based, or Linux®-based operating systems, or any othertype of operating system or firmware. The computing device may furtherbe configured to send and receive data over the Internet, short messageservice (SMS), email, and various other messaging applications and/orcommunication protocols. In particular embodiments, one or more softwareapplications may be installed on the computing device of a provider orrequestor, including an application associated with the transportationmanagement system 1002. The transportation application may, for example,be distributed by an entity associated with the transportationmanagement system via any distribution channel, such as an online sourcefrom which applications may be downloaded and/or via physical media,such as CDs and DVDs. Additional third-party applications unassociatedwith the transportation management system may also be installed on thecomputing device. In particular embodiments, the transportationapplication may communicate or share data and resources with one or moreof the installed third-party applications.

FIG. 11 illustrates an example block diagram of a transportationmanagement environment for matching ride requestors with autonomousvehicles. In particular embodiments, the environment may include variouscomputing entities, such as a user computing device 1130 of a user 1101(e.g., a ride provider or requestor), a transportation management system1160, an autonomous vehicle 1140, and one or more third-party system1170. The computing entities may be communicatively connected over anysuitable network 1110. As an example and not by way of limitation, oneor more portions of network 1110 may include an ad hoc network, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofPublic Switched Telephone Network (PSTN), a cellular network, or acombination of any of the above. In particular embodiments, any suitablenetwork arrangement and protocol enabling the computing entities tocommunicate with each other may be used. Although FIG. 11 illustrates asingle user device 1130, a single transportation management system 1160,a single vehicle 1140, a plurality of third-party systems 1170, and asingle network 1110, this disclosure contemplates any suitable number ofeach of these entities. As an example and not by way of limitation, thenetwork environment may include multiple users 1101, user devices 1130,transportation management systems 1160, autonomous-vehicles 1140,third-party systems 1170, and networks 1110.

The user device 1130, transportation management system 1160, autonomousvehicle 1140, and third-party system 1170 may be communicativelyconnected or co-located with each other in whole or in part. Thesecomputing entities may communicate via different transmissiontechnologies and network types. For example, the user device 1130 andthe vehicle 1140 may communicate with each other via a cable orshort-range wireless communication (e.g., Bluetooth, NFC, WI-FI, etc.),and together they may be connected to the Internet via a cellularnetwork that is accessible to either one of the devices (e.g., the userdevice 1130 may be a smartphone with LTE connection). The transportationmanagement system 1160 and third-party system 1170, on the other hand,may be connected to the Internet via their respective LAN/WLAN networksand Internet Service Providers (ISP). FIG. 11 illustrates transmissionlinks 1150 that connect user device 1130, autonomous vehicle 1140,transportation management system 1160, and third-party system 1170 tocommunication network 1110. This disclosure contemplates any suitabletransmission links 1150, including, e.g., wire connections (e.g., USB,Lightning, Digital Subscriber Line (DSL) or Data Over Cable ServiceInterface Specification (DOCSIS)), wireless connections (e.g., WI-FI,WiMAX, cellular, satellite, NFC, Bluetooth), optical connections (e.g.,Synchronous Optical Networking (SONET), Synchronous Digital Hierarchy(SDH)), any other wireless communication technologies, and anycombination thereof. In particular embodiments, one or more links 1150may connect to one or more networks 1110, which may include in part,e.g., ad-hoc network, the Intranet, extranet, VPN, LAN, WLAN, WAN, WWAN,MAN, PSTN, a cellular network, a satellite network, or any combinationthereof. The computing entities need not necessarily use the same typeof transmission link 1150. For example, the user device 1130 maycommunicate with the transportation management system via a cellularnetwork and the Internet, but communicate with the autonomous vehicle1140 via Bluetooth or a physical wire connection.

In particular embodiments, the transportation management system 1160 mayfulfill ride requests for one or more users 1101 by dispatching suitablevehicles. The transportation management system 1160 may receive anynumber of ride requests from any number of ride requestors 1101. Inparticular embodiments, a ride request from a ride requestor 1101 mayinclude an identifier that identifies the ride requestor in the system1160. The transportation management system 1160 may use the identifierto access and store the ride requestor's 1101 information, in accordancewith the requestor's 1101 privacy settings. The ride requestor's 1101information may be stored in one or more data stores (e.g., a relationaldatabase system) associated with and accessible to the transportationmanagement system 1160. In particular embodiments, ride requestorinformation may include profile information about a particular riderequestor 1101. In particular embodiments, the ride requestor 1101 maybe associated with one or more categories or types, through which theride requestor 1101 may be associated with aggregate information aboutcertain ride requestors of those categories or types. Ride informationmay include, for example, preferred pick-up and drop-off locations,driving preferences (e.g., safety comfort level, preferred speed, ratesof acceleration/deceleration, safety distance from other vehicles whentravelling at various speeds, route, etc.), entertainment preferencesand settings (e.g., preferred music genre or playlist, audio volume,display brightness, etc.), temperature settings, whether conversationwith the driver is welcomed, frequent destinations, historical ridingpatterns (e.g., time of day of travel, starting and ending locations,etc.), preferred language, age, gender, or any other suitableinformation. In particular embodiments, the transportation managementsystem 1160 may classify a user 1101 based on known information aboutthe user 1101 (e.g., using machine-learning classifiers), and use theclassification to retrieve relevant aggregate information associatedwith that class. For example, the system 1160 may classify a user 1101as a young adult and retrieve relevant aggregate information associatedwith young adults, such as the type of music generally preferred byyoung adults.

Transportation management system 1160 may also store and access rideinformation. Ride information may include locations related to the ride,traffic data, route options, optimal pick-up or drop-off locations forthe ride, or any other suitable information associated with a ride. Asan example and not by way of limitation, when the transportationmanagement system 1160 receives a request to travel from San FranciscoInternational Airport (SFO) to Palo Alto, Calif., the system 1160 mayaccess or generate any relevant ride information for this particularride request. The ride information may include, for example, preferredpick-up locations at SFO; alternate pick-up locations in the event thata pick-up location is incompatible with the ride requestor (e.g., theride requestor may be disabled and cannot access the pick-up location)or the pick-up location is otherwise unavailable due to construction,traffic congestion, changes in pick-up/drop-off rules, or any otherreason; one or more routes to navigate from SFO to Palo Alto; preferredoff-ramps for a type of user; or any other suitable informationassociated with the ride. In particular embodiments, portions of theride information may be based on historical data associated withhistorical rides facilitated by the system 1160. For example, historicaldata may include aggregate information generated based on past rideinformation, which may include any ride information described herein andtelemetry data collected by sensors in autonomous vehicles and/or userdevices. Historical data may be associated with a particular user (e.g.,that particular user's preferences, common routes, etc.), acategory/class of users (e.g., based on demographics), and/or all usersof the system 1160. For example, historical data specific to a singleuser may include information about past rides that particular user hastaken, including the locations at which the user is picked up anddropped off, music the user likes to listen to, traffic informationassociated with the rides, time of the day the user most often rides,and any other suitable information specific to the user. As anotherexample, historical data associated with a category/class of users mayinclude, e.g., common or popular ride preferences of users in thatcategory/class, such as teenagers preferring pop music, ride requestorswho frequently commute to the financial district may prefer to listen tothe news, etc. As yet another example, historical data associated withall users may include general usage trends, such as traffic and ridepatterns. Using historical data, the system 1160 in particularembodiments may predict and provide ride suggestions in response to aride request. In particular embodiments, the system 1160 may usemachine-learning, such as neural networks, regression algorithms,instance-based algorithms (e.g., k-Nearest Neighbor), decision-treealgorithms, Bayesian algorithms, clustering algorithms,association-rule-learning algorithms, deep-learning algorithms,dimensionality-reduction algorithms, ensemble algorithms, and any othersuitable machine-learning algorithms known to persons of ordinary skillin the art. The machine-learning models may be trained using anysuitable training algorithm, including supervised learning based onlabeled training data, unsupervised learning based on unlabeled trainingdata, and/or semi-supervised learning based on a mixture of labeled andunlabeled training data.

In particular embodiments, transportation management system 1160 mayinclude one or more server computers. Each server may be a unitaryserver or a distributed server spanning multiple computers or multipledatacenters. The servers may be of various types, such as, for exampleand without limitation, web server, news server, mail server, messageserver, advertising server, file server, application server, exchangeserver, database server, proxy server, another server suitable forperforming functions or processes described herein, or any combinationthereof. In particular embodiments, each server may include hardware,software, or embedded logic components or a combination of two or moresuch components for carrying out the appropriate functionalitiesimplemented or supported by the server. In particular embodiments,transportation management system 1160 may include one or more datastores. The data stores may be used to store various types ofinformation, such as ride information, ride requestor information, rideprovider information, historical information, third-party information,or any other suitable type of information. In particular embodiments,the information stored in the data stores may be organized according tospecific data structures. In particular embodiments, each data store maybe a relational, columnar, correlation, or any other suitable type ofdatabase system. Although this disclosure describes or illustratesparticular types of databases, this disclosure contemplates any suitabletypes of databases. Particular embodiments may provide interfaces thatenable a user device 1130 (which may belong to a ride requestor orprovider), a transportation management system 1160, vehicle system 1140,or a third-party system 1170 to process, transform, manage, retrieve,modify, add, or delete the information stored in the data store.

In particular embodiments, transportation management system 1160 mayinclude an authorization server (or any other suitable component(s))that allows users 1101 to opt-in to or opt-out of having theirinformation and actions logged, recorded, or sensed by transportationmanagement system 1160 or shared with other systems (e.g., third-partysystems 1170). In particular embodiments, a user 1101 may opt-in oropt-out by setting appropriate privacy settings. A privacy setting of auser may determine what information associated with the user may belogged, how information associated with the user may be logged, wheninformation associated with the user may be logged, who may loginformation associated with the user, whom information associated withthe user may be shared with, and for what purposes informationassociated with the user may be logged or shared. Authorization serversmay be used to enforce one or more privacy settings of the users 1101 oftransportation management system 1160 through blocking, data hashing,anonymization, or other suitable techniques as appropriate.

In particular embodiments, third-party system 1170 may be anetwork-addressable computing system that may provide HD maps or hostGPS maps, customer reviews, music or content, weather information, orany other suitable type of information. Third-party system 1170 maygenerate, store, receive, and send relevant data, such as, for example,map data, customer review data from a customer review website, weatherdata, or any other suitable type of data. Third-party system 1170 may beaccessed by the other computing entities of the network environmenteither directly or via network 1110. For example, user device 1130 mayaccess the third-party system 1170 via network 1110, or viatransportation management system 1160. In the latter case, ifcredentials are required to access the third-party system 1170, the user1101 may provide such information to the transportation managementsystem 1160, which may serve as a proxy for accessing content from thethird-party system 1170.

In particular embodiments, user device 1130 may be a mobile computingdevice such as a smartphone, tablet computer, or laptop computer. Userdevice 1130 may include one or more processors (e.g., CPU and/or GPU),memory, and storage. An operating system and applications may beinstalled on the user device 1130, such as, e.g., a transportationapplication associated with the transportation management system 1160,applications associated with third-party systems 1170, and applicationsassociated with the operating system. User device 1130 may includefunctionality for determining its location, direction, or orientation,based on integrated sensors such as GPS, compass, gyroscope, oraccelerometer. User device 1130 may also include wireless transceiversfor wireless communication and may support wireless communicationprotocols such as Bluetooth, near-field communication (NFC), infrared(IR) communication, WI-FI, and/or 2G/3G/4G/LTE mobile communicationstandard. User device 1130 may also include one or more cameras,scanners, touchscreens, microphones, speakers, and any other suitableinput-output devices.

In particular embodiments, the vehicle 1140 may be an autonomous vehicleand equipped with an array of sensors 1144, a navigation system 1146,and a ride-service computing device 1148. In particular embodiments, afleet of autonomous vehicles 1140 may be managed by the transportationmanagement system 1160. The fleet of autonomous vehicles 1140, in wholeor in part, may be owned by the entity associated with thetransportation management system 1160, or they may be owned by athird-party entity relative to the transportation management system1160. In either case, the transportation management system 1160 maycontrol the operations of the autonomous vehicles 1140, including, e.g.,dispatching select vehicles 1140 to fulfill ride requests, instructingthe vehicles 1140 to perform select operations (e.g., head to a servicecenter or charging/fueling station, pull over, stop immediately,self-diagnose, lock/unlock compartments, change music station, changetemperature, and any other suitable operations), and instructing thevehicles 1140 to enter select operation modes (e.g., operate normally,drive at a reduced speed, drive under the command of human operators,and any other suitable operational modes).

In particular embodiments, the autonomous vehicles 1140 may receive datafrom and transmit data to the transportation management system 1160 andthe third-party system 1170. Example of received data may include, e.g.,instructions, new software or software updates, maps, 3D models, trainedor untrained machine-learning models, location information (e.g.,location of the ride requestor, the autonomous vehicle 1140 itself,other autonomous vehicles 1140, and target destinations such as servicecenters), navigation information, traffic information, weatherinformation, entertainment content (e.g., music, video, and news) riderequestor information, ride information, and any other suitableinformation. Examples of data transmitted from the autonomous vehicle1140 may include, e.g., telemetry and sensor data,determinations/decisions based on such data, vehicle condition or state(e.g., battery/fuel level, tire and brake conditions, sensor condition,speed, odometer, etc.), location, navigation data, passenger inputs(e.g., through a user interface in the vehicle 1140, passengers maysend/receive data to the transportation management system 1160 and/orthird-party system 1170), and any other suitable data.

In particular embodiments, autonomous vehicles 1140 may also communicatewith each other as well as other traditional human-driven vehicles,including those managed and not managed by the transportation managementsystem 1160. For example, one vehicle 1140 may communicate with anothervehicle data regarding their respective location, condition, status,sensor reading, and any other suitable information. In particularembodiments, vehicle-to-vehicle communication may take place over directshort-range wireless connection (e.g., WI-FI, Bluetooth, NFC) and/orover a network (e.g., the Internet or via the transportation managementsystem 1160 or third-party system 1170).

In particular embodiments, an autonomous vehicle 1140 may obtain andprocess sensor/telemetry data. Such data may be captured by any suitablesensors. For example, the vehicle 1140 may have aa Light Detection andRanging (LiDAR) sensor array of multiple LiDAR transceivers that areconfigured to rotate 360°, emitting pulsed laser light and measuring thereflected light from objects surrounding vehicle 1140. In particularembodiments, LiDAR transmitting signals may be steered by use of a gatedlight valve, which may be a MEMs device that directs a light beam usingthe principle of light diffraction. Such a device may not use a gimbaledmirror to steer light beams in 360° around the autonomous vehicle.Rather, the gated light valve may direct the light beam into one ofseveral optical fibers, which may be arranged such that the light beammay be directed to many discrete positions around the autonomousvehicle. Thus, data may be captured in 360° around the autonomousvehicle, but no rotating parts may be necessary. A LiDAR is an effectivesensor for measuring distances to targets, and as such may be used togenerate a three-dimensional (3D) model of the external environment ofthe autonomous vehicle 1140. As an example and not by way of limitation,the 3D model may represent the external environment including objectssuch as other cars, curbs, debris, objects, and pedestrians up to amaximum range of the sensor arrangement (e.g., 50, 100, or 200 meters).As another example, the autonomous vehicle 1140 may have optical cameraspointing in different directions. The cameras may be used for, e.g.,recognizing roads, lane markings, street signs, traffic lights, police,other vehicles, and any other visible objects of interest. To enable thevehicle 1140 to “see” at night, infrared cameras may be installed. Inparticular embodiments, the vehicle may be equipped with stereo visionfor, e.g., spotting hazards such as pedestrians or tree branches on theroad. As another example, the vehicle 1140 may have radars for, e.g.,detecting other vehicles and/or hazards afar. Furthermore, the vehicle1140 may have ultrasound equipment for, e.g., parking and obstacledetection. In addition to sensors enabling the vehicle 1140 to detect,measure, and understand the external world around it, the vehicle 1140may further be equipped with sensors for detecting and self-diagnosingthe vehicle's own state and condition. For example, the vehicle 1140 mayhave wheel sensors for, e.g., measuring velocity; global positioningsystem (GPS) for, e.g., determining the vehicle's current geolocation;and/or inertial measurement units, accelerometers, gyroscopes, and/orodometer systems for movement or motion detection. While the descriptionof these sensors provides particular examples of utility, one ofordinary skill in the art would appreciate that the utilities of thesensors are not limited to those examples. Further, while an example ofa utility may be described with respect to a particular type of sensor,it should be appreciated that the utility may be achieved using anycombination of sensors. For example, an autonomous vehicle 1140 maybuild a 3D model of its surrounding based on data from its LiDAR, radar,sonar, and cameras, along with a pre-generated map obtained from thetransportation management system 1160 or the third-party system 1170.Although sensors 1144 appear in a particular location on autonomousvehicle 1140 in FIG. 11, sensors 1144 may be located in any suitablelocation in or on autonomous vehicle 1140. Example locations for sensorsinclude the front and rear bumpers, the doors, the front windshield, onthe side panel, or any other suitable location.

In particular embodiments, the autonomous vehicle 1140 may be equippedwith a processing unit (e.g., one or more CPUs and GPUs), memory, andstorage. The vehicle 1140 may thus be equipped to perform a variety ofcomputational and processing tasks, including processing the sensordata, extracting useful information, and operating accordingly. Forexample, based on images captured by its cameras and a machine-visionmodel, the vehicle 1140 may identify particular types of objectscaptured by the images, such as pedestrians, other vehicles, lanes,curbs, and any other objects of interest.

In particular embodiments, the autonomous vehicle 1140 may have anavigation system 1146 responsible for safely navigating the autonomousvehicle 1140. In particular embodiments, the navigation system 1146 maytake as input any type of sensor data from, e.g., a Global PositioningSystem (GPS) module, inertial measurement unit (IMU), LiDAR sensors,optical cameras, radio frequency (RF) transceivers, or any othersuitable telemetry or sensory mechanisms. The navigation system 1146 mayalso utilize, e.g., map data, traffic data, accident reports, weatherreports, instructions, target destinations, and any other suitableinformation to determine navigation routes and particular drivingoperations (e.g., slowing down, speeding up, stopping, swerving, etc.).In particular embodiments, the navigation system 1146 may use itsdeterminations to control the vehicle 1140 to operate in prescribedmanners and to guide the autonomous vehicle 1140 to its destinationswithout colliding into other objects. Although the physical embodimentof the navigation system 1146 (e.g., the processing unit) appears in aparticular location on autonomous vehicle 1140 in FIG. 11, navigationsystem 1146 may be located in any suitable location in or on autonomousvehicle 1140. Example locations for navigation system 1146 includeinside the cabin or passenger compartment of autonomous vehicle 1140,near the engine/battery, near the front seats, rear seats, or in anyother suitable location.

In particular embodiments, the autonomous vehicle 1140 may be equippedwith a ride-service computing device 1148, which may be a tablet or anyother suitable device installed by transportation management system 1160to allow the user to interact with the autonomous vehicle 1140,transportation management system 1160, other users 1101, or third-partysystems 1170. In particular embodiments, installation of ride-servicecomputing device 1148 may be accomplished by placing the ride-servicecomputing device 1148 inside autonomous vehicle 1140, and configuring itto communicate with the vehicle 1140 via a wire or wireless connection(e.g., via Bluetooth). Although FIG. 11 illustrates a singleride-service computing device 1148 at a particular location inautonomous vehicle 1140, autonomous vehicle 1140 may include severalride-service computing devices 1148 in several different locationswithin the vehicle. As an example and not by way of limitation,autonomous vehicle 1140 may include four ride-service computing devices1148 located in the following places: one in front of the front-leftpassenger seat (e.g., driver's seat in traditional U.S. automobiles),one in front of the front-right passenger seat, one in front of each ofthe rear-left and rear-right passenger seats. In particular embodiments,ride-service computing device 1148 may be detachable from any componentof autonomous vehicle 1140. This may allow users to handle ride-servicecomputing device 1148 in a manner consistent with other tablet computingdevices. As an example and not by way of limitation, a user may moveride-service computing device 1148 to any location in the cabin orpassenger compartment of autonomous vehicle 1140, may hold ride-servicecomputing device 1148, or handle ride-service computing device 1148 inany other suitable manner. Although this disclosure describes providinga particular computing device in a particular manner, this disclosurecontemplates providing any suitable computing device in any suitablemanner.

FIG. 12 illustrates an example computer system. In particularembodiments, one or more computer systems 1200 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1200 provide thefunctionalities described or illustrated herein. In particularembodiments, software running on one or more computer systems 1200performs one or more steps of one or more methods described orillustrated herein or provides the functionalities described orillustrated herein. Particular embodiments include one or more portionsof one or more computer systems 1200. Herein, a reference to a computersystem may encompass a computing device, and vice versa, whereappropriate. Moreover, a reference to a computer system may encompassone or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1200. This disclosure contemplates computer system 1200 taking anysuitable physical form. As example and not by way of limitation,computer system 1200 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, an augmented/virtual reality device, or a combinationof two or more of these. Where appropriate, computer system 1200 mayinclude one or more computer systems 1200; be unitary or distributed;span multiple locations; span multiple machines; span multiple datacenters; or reside in a cloud, which may include one or more cloudcomponents in one or more networks. Where appropriate, one or morecomputer systems 1200 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 1200 may perform in real-time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 1200 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 1200 includes a processor1202, memory 1204, storage 1206, an input/output (I/O) interface 1208, acommunication interface 1210, and a bus 1212. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1202 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1202 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1204, or storage 1206; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1204, or storage 1206. In particularembodiments, processor 1202 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1202 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1202 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1204 or storage 1206, and the instruction caches may speed upretrieval of those instructions by processor 1202. Data in the datacaches may be copies of data in memory 1204 or storage 1206 that are tobe operated on by computer instructions; the results of previousinstructions executed by processor 1202 that are accessible tosubsequent instructions or for writing to memory 1204 or storage 1206;or any other suitable data. The data caches may speed up read or writeoperations by processor 1202. The TLBs may speed up virtual-addresstranslation for processor 1202. In particular embodiments, processor1202 may include one or more internal registers for data, instructions,or addresses. This disclosure contemplates processor 1202 including anysuitable number of any suitable internal registers, where appropriate.Where appropriate, processor 1202 may include one or more arithmeticlogic units (ALUs), be a multi-core processor, or include one or moreprocessors 1202. Although this disclosure describes and illustrates aparticular processor, this disclosure contemplates any suitableprocessor.

In particular embodiments, memory 1204 includes main memory for storinginstructions for processor 1202 to execute or data for processor 1202 tooperate on. As an example and not by way of limitation, computer system1200 may load instructions from storage 1206 or another source (such asanother computer system 1200) to memory 1204. Processor 1202 may thenload the instructions from memory 1204 to an internal register orinternal cache. To execute the instructions, processor 1202 may retrievethe instructions from the internal register or internal cache and decodethem. During or after execution of the instructions, processor 1202 maywrite one or more results (which may be intermediate or final results)to the internal register or internal cache. Processor 1202 may thenwrite one or more of those results to memory 1204. In particularembodiments, processor 1202 executes only instructions in one or moreinternal registers or internal caches or in memory 1204 (as opposed tostorage 1206 or elsewhere) and operates only on data in one or moreinternal registers or internal caches or in memory 1204 (as opposed tostorage 1206 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 1202 tomemory 1204. Bus 1212 may include one or more memory buses, as describedin further detail below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1202 and memory 1204and facilitate accesses to memory 1204 requested by processor 1202. Inparticular embodiments, memory 1204 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1204 may include one ormore memories 1204, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1206 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1206 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1206 may include removable or non-removable (or fixed)media, where appropriate. Storage 1206 may be internal or external tocomputer system 1200, where appropriate. In particular embodiments,storage 1206 is non-volatile, solid-state memory. In particularembodiments, storage 1206 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1206taking any suitable physical form. Storage 1206 may include one or morestorage control units facilitating communication between processor 1202and storage 1206, where appropriate. Where appropriate, storage 1206 mayinclude one or more storages 1206. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1208 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1200 and one or more I/O devices. Computersystem 1200 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1200. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1208 for them. Where appropriate, I/Ointerface 1208 may include one or more device or software driversenabling processor 1202 to drive one or more of these I/O devices. I/Ointerface 1208 may include one or more I/O interfaces 1208, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1210 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1200 and one or more other computer systems 1200 or oneor more networks. As an example and not by way of limitation,communication interface 1210 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or any otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1210 for it. As an example and not by way oflimitation, computer system 1200 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1200 may communicate with awireless PAN (WPAN) (such as, for example, a Bluetooth WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orany other suitable wireless network or a combination of two or more ofthese. Computer system 1200 may include any suitable communicationinterface 1210 for any of these networks, where appropriate.Communication interface 1210 may include one or more communicationinterfaces 1210, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1212 includes hardware, software, or bothcoupling components of computer system 1200 to each other. As an exampleand not by way of limitation, bus 1212 may include an AcceleratedGraphics Port (AGP) or any other graphics bus, an Enhanced IndustryStandard Architecture (EISA) bus, a front-side bus (FSB), aHYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture(ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, amemory bus, a Micro Channel Architecture (MCA) bus, a PeripheralComponent Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serialadvanced technology attachment (SATA) bus, a Video Electronics StandardsAssociation local (VLB) bus, or another suitable bus or a combination oftwo or more of these. Bus 1212 may include one or more buses 1212, whereappropriate. Although this disclosure describes and illustrates aparticular bus, this disclosure contemplates any suitable bus orinterconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other types of integratedcircuits (ICs) (such, as for example, field-programmable gate arrays(FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs),hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

1. A method comprising, by a computing system: determining a measureddriving characteristic of a driving control system based on observationsof one or more vehicles driven by the driving control system;determining a difference between the measured driving characteristic anda target driving characteristic, wherein the target drivingcharacteristic is based on objectivations of one or more manuallycontrolled vehicles; determining an evaluation objective for the drivingcontrol system; determining a weight function for the evaluationobjective; determining a score for the driving control system withrespect to the evaluation objective by weighting the difference betweenthe measured driving characteristic and the target drivingcharacteristic using the weight function; and applying, based on thescore, an adjustment to the driving control system to reduce adifference between a subsequently measured driving characteristic of thedriving control system and the target driving characteristic.
 2. Themethod of claim 1, wherein the weight function for the evaluationobjective has a profile that is tailored based on the evaluationobjective, and wherein the score for the driving control system dependson the evaluation objective.
 3. The method of claim 1, wherein thedifference between the subsequently measured driving characteristic ofthe driving control system and the target driving characteristic isdetermined based on a driving parameter distribution associated with theone or more vehicles driven by the driving control system.
 4. The methodof claim 1, further comprising: determining a road segment on which theone or more vehicles are driven by the driving control system, whereinthe adjustment to reduce the difference between the subsequentlymeasured driving characteristic of the driving control system and thetarget driving characteristic is determined based on one or moreconditions of the road segment.
 5. The method of claim 1, wherein theadjustment is applied to the driving control system in response todetermining that the score for the driving control system fails tosatisfy a pre-determined threshold.
 6. The method of claim 5, furthercomprising: determining a scenario in a surrounding environmentencountered by the one or more vehicles driven by the driving controlsystem, and wherein the pre-determined threshold for the score isdetermined based on the scenario.
 7. The method of claim 6, wherein theadjustment to the driving control system is determined based on thescenario encountered by the one or more vehicles in the surroundingenvironment.
 8. The method of claim 1, further comprising: receiving auser input evaluating performance of the one or more vehicles driven bythe driving control system, wherein the weight function is based on theuser input.
 9. The method of claim 1, wherein the adjustment to thedriving control system is applied in real-time while the one or morevehicles are driven by the driving control system.
 10. The method ofclaim 1, further comprising: communicating the adjustment to a pluralityof vehicles that are driven by a same version of the driving controlsystem; and causing the plurality of vehicles driven by the same versionof the driving control system to apply the adjustment.
 11. The method ofclaim 1, wherein the difference between the measured drivingcharacteristic and the target driving characteristic is determined byoverlaying a distribution of a measured driving parameter to a targetdistribution of the measured driving parameter.
 12. The method of claim1, wherein the difference between the measured driving characteristicand the target driving characteristic is determined based on one or moredriving parameters comprising one or more of: an amount of decelerationor acceleration, a rate of acceleration, a steering angle, an angularmomentum, a velocity, a distance to a nearest object, a distance to alane edge, a distance to a center line, a distance to an intersection,or an reaction of a nearby driver.
 13. The method of claim 1, whereinthe evaluation objective is selected from a plurality of pre-determinedevaluation objectives for evaluating performance of the one or morevehicles, and wherein the plurality of pre-determined evaluationobjectives comprise one or more of: an elegance, an efficiency, or acomfort.
 14. One or more computer-readable non-transitory storage mediaembodying software that is operable when executed to cause one or moreprocessors to perform operations comprising: determining a measureddriving characteristic of a driving control system based on observationsof one or more vehicles driven by the driving control system;determining a difference between the measured driving characteristic anda target driving characteristic, wherein the target drivingcharacteristic is based on objectivations of one or more manuallycontrolled vehicles; determining an evaluation objective for the drivingcontrol system; determining a weight function for the evaluationobjective; determining a score for the driving control system withrespect to the evaluation objective by weighting the difference betweenthe measured driving characteristic and the target drivingcharacteristic using the weight function; and applying, based on thescore, an adjustment to the driving control system to reduce adifference between a subsequently measured driving characteristic of thedriving control system and the target driving characteristic.
 15. Themedia of claim 14 wherein the weight function for the evaluationobjective has a profile that is tailored based on the evaluationobjective, and wherein the score for the driving control system dependson the evaluation objective.
 16. The media of claim 14, wherein thedifference between the subsequently measured driving characteristic ofthe driving control system and the target driving characteristic isdetermined based on a driving parameter distribution associated with theone or more vehicles driven by the driving control system.
 17. The mediaof claim 13, wherein the media further embodies software that isoperable to perform operations comprising: determining a road segment onwhich the one or more vehicles are driven by the driving control system,wherein the adjustment to reduce the difference between the subsequentlymeasured driving characteristic of the driving control system and thetarget driving characteristic is determined based on one or moreconditions of the road segment.
 18. A computing system comprising: oneor more processors; and one or more computer-readable non-transitorystorage media coupled to one or more of the processors, the one or morecomputer-readable non-transitory storage media comprising instructionsoperable when executed by one or more of the processors to cause thecomputing system to perform operations comprising: determining ameasured driving characteristic of a driving control system based onobservations of one or more vehicles driven by the driving controlsystem; determining a difference between the measured drivingcharacteristic and a target driving characteristic, wherein the targetdriving characteristic is based on objectivations of one or moremanually controlled vehicles; determining an evaluation objective forthe driving control system; determining a weight function for theevaluation objective; determining a score for the driving control systemwith respect to the evaluation objective by weighting the differencebetween the measured driving characteristic and the target drivingcharacteristic using the weight function; and applying, based on thescore, an adjustment to the driving control system to reduce adifference between a subsequently measured driving characteristic of thedriving control system and the target driving characteristic.
 19. Thesystem of claim 18, wherein the weight function for the evaluationobjective has a profile that is tailored based on the evaluationobjective, and wherein the score for the driving control system dependson the evaluation objective.
 20. The system of claim 18, wherein thedifference between the subsequently measured driving characteristic ofthe driving control system and the target driving characteristic isdetermined based on a driving parameter distribution associated with theone or more vehicles driven by the driving control system.